The following meanings for the abbreviations used in this specification apply:    ARTIST4G—Advanced Radio Interface TechnologIes for 4G SysTems    BVDM—Building Vector Data Map    CA—Cooperation Area    CBR—Constant Bit Rate    CC—Channel Component    CoMP—Cooperative multipoint transmission    C-RNTI—Cell Radio Network Temporary Identifier    CS—Coordinated Scheduling    CSI—Channel state information    DL—Downlink    ENB—enhanced node B    IRC—Interference Rejection Receiver    JT—Joint Transmission    JT CoMP—Joint Transmission CoMP    PRB—Physical resource block    PDCCH—Physical DL control channel    PDSCH—Physical DL shared channel    PMI—Precoding Matrix Index    PNL—Power Normalization Loss    PUCCH—Physical UL control channel    PUSCH—Physical UL shared channel    QoS—Quality of Service    RRM—Radio Resource Management    RSRP—Reference Signal Received Power    RX—Receiver    SF—Subframe    SINR—Signal to Interference plus Noise Ratio    SoA—Service oriented Architecture    TX—Transmitter    UE—User equipment    UL—Uplink
Embodiments of the present invention relate to the future evolution of mobile radio systems going beyond LTE Advanced so called 5G systems. Focus is on a robust integration of joint transmission—cooperative multipoint (JT CoMP) into an overall interference mitigation framework, which is assumed to integrate small cells into a wide area network. An important further aspect is an overall small feedback overhead for CSI reporting.
Robustness—especially in the context of channel prediction—is very important, as channel prediction is typically unreliable and it can be assumed that standardization of such a technique for the support of JT CoMP will be difficult without a predictable performance.
In the EU funded project Artist4G a so called interference mitigation framework (IMF-A) (1. ARTIST4G consortium, “D1.4—Interference Avoidance techniques and system design,” project report, July, 2012) has been developed and investigated providing for interference limited scenarios significant performance gains (e.g. more than 100%) under the assumption of perfect channel knowledge. IMF-A includes several techniques like JT CoMP, interference floor shaping and specific user grouping and CoMP scheduling techniques.
The main challenge is JT CoMP due to its sensitivity to channel state information (CSI) estimation and prediction errors, especially for fast moving UEs. At the same time JT COMP is often the most powerful technology to overcome—or even to exploit—strong interference conditions generated by neighbouring sites, which ensures gains even for fully or heavily loaded systems.
In real world multi site measurements—as well as in recent METIS investigations e.g. from HHI—, state of the art channel prediction helped to keep a more or less significant part of these performance gains even for mobile users, verifying the potential of the IMF-A framework.
A typical cooperation area of the IMF-A framework of size nine cells with e.g. four antenna elements each will comprise already 36 channel components per user equipment (UE), leading to a high sensitivity against CSI reporting and corresponding precoding errors. One reason for this sensitivity is the high number of relevant channel components (CC) and each one will contribute more or less degradations due to prediction errors. Fortunately many of the channel components are weak, which allows partial reporting of CCs above a certain threshold. Nonetheless the number of relevant CCs is still quite high.
Massive MIMO—as proposed for 5G wide area—might help to reduce the number of relevant channel components by focusing energy to the intended users. Contrarily the inclusion of small cells into an overall interference mitigation framework will put new challenges onto the channel reporting and precoding due to the potentially high number of small cells per wide area macro cell. Note, here in band service of small cells is being assumed.
One way to overcome the unreliability of channel prediction is to limit the prediction horizon to one or few tenth of the RF wavelength λ (carrier wavelength). This puts a natural limit on the mobile speed to one to few kmh or has to be paid by quite some performance losses.
Robust precoding as proposed in Artist4G is based on additional reliability feedback per channel component allowing to adapt the JT precoder to the expected uncertainties of the channel components. This exploits a given channel knowledge as far as possible, but has to be paid by an adaptation to the expected worst case performance, i.e. few unreliable channel components might generate significant performance losses.
It has been shown that advanced receivers—consisting of more than one antenna element—may partly adapt to the precoding errors so that the usable prediction horizon for JT CoMP can be increased.
For feedback compression many techniques are known in the meantime like lossless compression, tracking solutions, codebook based precoding, etc., but still reporting overhead will be relatively high or precoding performance will be degraded due to limited channel knowledge.
SoA for channel prediction includes Wiener- or Kalman filtering and geometrical models with parameter based channel prediction. From literature even optimum Kalman filtering has a limited prediction horizon of 0.1 to few tenth of λ (see D. Aronsson, “Channel estimation and prediction for MIMO OFDM systems—Key design and performance aspects of Kalman-based algorithms”, Ph.D Thesis, Dept. of Engineering Sciences, Signals and Systems Group, Uppsala University, March 2011).